This script calculates the correlations between different cell entry selections. Either the median of all LibA selections vs the median of all LibB selections, OR all selections for a specific condition.¶
In [1]:
# this cell is tagged as parameters for `papermill` parameterization
altair_config = None
nipah_config = None
codon_variants_file = None
CHO_corr_plot_save = None
CHO_EFNB2_indiv_plot_save = None
CHO_EFNB3_indiv_plot_save = None
histogram_plot = None
func_scores_plot = None
uniq_barcodes_per_lib_df = None
In [2]:
# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
codon_variants_file = "results/variants/codon_variants.csv"
CHO_corr_plot_save = "results/images/CHO_corr_plot_save.html"
CHO_EFNB2_indiv_plot_save = "results/images/CHO_EFNB2_all_corrs.html"
CHO_EFNB3_indiv_plot_save = "results/images/CHO_EFNB3_all_corrs.html"
histogram_plot = "results/images/variants_histogram.html"
func_scores_plot = "results/images/func_scores_distribution.html"
uniq_barcodes_per_lib_df = "results/tables/uniq_barcodes_per_lib_df.csv"
In [3]:
import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import Bio.SeqIO
import yaml
from Bio import AlignIO
from Bio import PDB
from Bio.Align import PairwiseAligner
from collections import Counter
In [4]:
# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()
if os.getcwd() == '/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/':
pass
print("Already in correct directory")
else:
os.chdir("/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/")
print("Setup in correct directory")
Setup in correct directory
In [5]:
if histogram_plot is None:
altair_config = 'data/custom_analyses_data/theme.py'
nipah_config = 'nipah_config.yaml'
codon_variants_file = 'results/variants/codon_variants.csv'
#CHO_corr_plot_save
#CHO_EFNB3_corr_plot_save
#CHO_EFNB2_indiv_plot_save
#CHO_EFNB3_indiv_plot_save
In [6]:
if altair_config:
with open(altair_config, 'r') as file:
exec(file.read())
with open(nipah_config) as f:
config = yaml.safe_load(f)
with open('data/func_effects_config.yml') as y:
config_func = yaml.safe_load(y)
In [7]:
cho_efnb2_low_selections = config_func['avg_func_effects']['CHO_EFNB2_low']['selections']
LibA_CHO_EFNB2 = [selection + '_func_effects.csv' for selection in cho_efnb2_low_selections if 'LibA' in selection and 'LibB' not in selection]
LibB_CHO_EFNB2 = [selection + '_func_effects.csv' for selection in cho_efnb2_low_selections if 'LibB' in selection and 'LibA' not in selection]
cho_efnb3_low_selections = config_func['avg_func_effects']['CHO_EFNB3_low']['selections']
LibA_CHO_EFNB3 = [selection + '_func_effects.csv' for selection in cho_efnb3_low_selections if 'LibA' in selection and 'LibB' not in selection]
LibB_CHO_EFNB3 = [selection + '_func_effects.csv' for selection in cho_efnb3_low_selections if 'LibB' in selection and 'LibA' not in selection]
Calculate correlations for LibA and LibB for CHO-EFNB2 cell entry selections¶
In [8]:
path = 'results/func_effects/by_selection/'
def process_func_selections(library,library_name):
df_list = []
clock = 1
for file_name in library:
file_path = os.path.join(path, file_name)
fixed_name = file_name.replace('_func_effects.csv', '')
# Read the current CSV file
func_scores = pd.read_csv(file_path)
func_scores_renamed = func_scores.rename(columns={'functional_effect': f'functional_effect_{clock}','times_seen': f'times_seen_{clock}'})
func_scores_renamed.drop(['latent_phenotype_effect'],axis=1,inplace=True)
# Append the dataframe to the list
df_list.append(func_scores_renamed)
clock = clock + 1
# Merge all dataframes on 'site' and 'mutant'
merged_df = df_list[0]
for df in df_list[1:]:
merged_df = pd.merge(merged_df, df, on=['site', 'mutant','wildtype'], how='outer')
#Calculate median values
lib_columns_func = [col for col in merged_df.columns if 'functional_effect' in col]
merged_df[f'median_effect_{library_name}'] = merged_df[lib_columns_func].median(axis=1)
lib_columns_times_seen = [col for col in merged_df.columns if 'times_seen' in col]
merged_df[f'median_times_seen_{library_name}'] = merged_df[lib_columns_times_seen].median(axis=1)
#Now drop columns
lib_columns = [col for col in merged_df.columns if re.search(r'_\d+', col)]
merged_df = merged_df.drop(columns=lib_columns)
return merged_df
A_selections_E2 = process_func_selections(LibA_CHO_EFNB2,'LibA')
B_selections_E2 = process_func_selections(LibB_CHO_EFNB2,'LibB')
A_selections_E3 = process_func_selections(LibA_CHO_EFNB3,'LibA')
B_selections_E3 = process_func_selections(LibB_CHO_EFNB3,'LibB')
def merge_selections(A_selections,B_selections):
merged_selections = pd.merge(A_selections,B_selections,on=['wildtype','site','mutant'],how='inner')
#make one times seen column for slider
lib_columns_times_seen = [col for col in merged_selections.columns if 'times_seen' in col]
merged_selections['times_seen'] = merged_selections[lib_columns_times_seen].median(axis=1)
return merged_selections
CHO_EFNB2_merged = merge_selections(A_selections_E2,B_selections_E2)
CHO_EFNB2_merged['cell_type'] = 'CHO-EFNB2'
CHO_EFNB3_merged = merge_selections(A_selections_E3,B_selections_E3)
CHO_EFNB3_merged['cell_type'] = 'CHO-EFNB3'
both_selections = pd.concat([CHO_EFNB2_merged, CHO_EFNB3_merged])
def make_chart_median(df,title):
slider = alt.binding_range(min=1, max=25, step=1, name="times_seen")
selector = alt.param(name="SelectorName", value=1, bind=slider)
empty = []
variant_selector = alt.selection_point(
on="mouseover",
empty=False,
nearest=True,
fields=["site","mutant"],
value=1
)
df = df[
(df['median_effect_LibA'].notna()) &
(df['median_effect_LibB'].notna())
]
size = df.shape[0]
for selection in ['CHO-EFNB2','CHO-EFNB3']:
print(selection)
tmp_df = df[df['cell_type'] == selection]
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(df[f'median_effect_LibA'], df[f'median_effect_LibB'])
r_value = float(r_value)
print(f'{r_value:.2f}')
chart = alt.Chart(tmp_df,title=f'Entry in {selection} cells').mark_point().encode(
x=alt.X('median_effect_LibA',title='LibA Cell Entry'),
y=alt.Y('median_effect_LibB',title='LibB Cell Entry'),
tooltip=['site','wildtype','mutant','times_seen'],
size=alt.condition(variant_selector, alt.value(100),alt.value(15)),
color=alt.condition(alt.datum.times_seen < selector, alt.value('transparent'), alt.value('black')),
opacity=alt.condition(variant_selector, alt.value(1),alt.value(0.2)),
)
empty.append(chart)
combined_effect_chart = alt.hconcat(*empty).resolve_scale(y='shared', x='shared', color='independent').add_params(variant_selector,selector)
return combined_effect_chart
CHO_EFNB2_corr_plot = make_chart_median(both_selections,'CHO-EFNB2')
CHO_EFNB2_corr_plot.display()
if histogram_plot is not None:
CHO_EFNB2_corr_plot.save(CHO_corr_plot_save)
CHO-EFNB2 0.92 CHO-EFNB3 0.92
In [9]:
def plot_corr_heatmap(df):
empty_chart = []
for cell in list(df['cell_type'].unique()):
tmp_df = df[df['cell_type'] == cell]
chart = alt.Chart(tmp_df,title=f'{cell}').mark_rect().encode(
x=alt.X('median_effect_LibA',title='Library A').bin(maxbins=50), #axis=alt.Axis(values=[-4,-1,0,1])
y=alt.Y('median_effect_LibB',title='Library B').bin(maxbins=50), #,axis=alt.Axis(values=[-4,-1,0,1])
color=alt.Color('count()',title='Count').scale(scheme='greenblue'),
#tooltip=['effect','binding_median']
)
empty_chart.append(chart)
combined_chart = alt.hconcat(*empty_chart,title=alt.Title('Correlation between binding and entry')).resolve_scale(y='shared',x='shared',color='shared')
return combined_chart
entry_binding_corr_heatmap = plot_corr_heatmap(both_selections)
entry_binding_corr_heatmap.display()
#entry_binding_corr_heatmap.save(entry_binding_corr_heatmap)
Make correlations between individual selections¶
In [10]:
#path = 'results/func_effects/by_selection/'
def process_individ_selections(library):
df_list = []
clock = 1
for file_name in library:
file_path = os.path.join(path, file_name)
print(f"Processing file: {file_path}")
#fixed_name = file_name.replace('_func_effects.csv', '')
# Read the current CSV file
func_scores = pd.read_csv(file_path)
#display(func_scores.head(2))
#func_scores = func_scores[func_scores['times_seen'] >= config['func_times_seen_cutoff']]
func_scores_renamed = func_scores.rename(columns={'functional_effect': f'functional_effect_{clock}','times_seen': f'times_seen_{clock}'})
func_scores_renamed.drop(['latent_phenotype_effect'],axis=1,inplace=True)
# Append the dataframe to the list
df_list.append(func_scores_renamed)
clock = clock + 1
# Merge all dataframes on 'site' and 'mutant'
merged_df = df_list[0]
for df in df_list[1:]:
merged_df = pd.merge(merged_df, df, on=['site', 'mutant','wildtype'], how='outer')
# Make list of how many selections are done for later correlation plots
lib_size = len(library)
number_list = [i for i in range(1, lib_size+1)]
return merged_df,number_list
CHO_EFNB2_indiv,lib_size_EFNB2 = process_individ_selections(LibA_CHO_EFNB2+LibB_CHO_EFNB2)
CHO_EFNB3_indiv,lib_size_EFNB3 = process_individ_selections(LibA_CHO_EFNB3+LibB_CHO_EFNB3)
def make_chart(df,number_list):
empty_list = []
for i in number_list:
other_empty_list = []
for j in number_list:
df = df[
(df[f'times_seen_{i}'] >= config['func_times_seen_cutoff']) &
(df[f'times_seen_{j}'] >= config['func_times_seen_cutoff']) &
(df[f'functional_effect_{i}'].notna()) &
(df[f'functional_effect_{j}'].notna())
]
#slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(df[f'functional_effect_{i}'], df[f'functional_effect_{j}'])
#r_value = float(r_value)
#print(f'{r_value:.2f}')
chart = alt.Chart(df).mark_circle(size=10, color='black', opacity=0.2).encode(
x=alt.X(f'functional_effect_{i}'),
y=alt.Y(f'functional_effect_{j}'),
tooltip=['site','wildtype','mutant'],
).properties(
height=alt.Step(10),
width=alt.Step(10)
)
other_empty_list.append(chart)
combined_effect_chart = alt.hconcat(*other_empty_list).resolve_scale(y='shared', x='shared', color='independent')
empty_list.append(combined_effect_chart)
final_combined_chart = alt.vconcat(*empty_list).resolve_scale(y='shared', x='shared', color='independent')
return final_combined_chart
CHO_EFNB2_indiv_plot = make_chart(CHO_EFNB2_indiv,lib_size_EFNB2)
#CHO_EFNB2_indiv_plot.display()
if histogram_plot is not None:
CHO_EFNB2_indiv_plot.save(CHO_EFNB2_indiv_plot_save)
CHO_EFNB3_indiv_plot = make_chart(CHO_EFNB3_indiv,lib_size_EFNB3)
if histogram_plot is not None:
CHO_EFNB3_indiv_plot.save(CHO_EFNB3_indiv_plot_save)
Processing file: results/func_effects/by_selection/LibA-231112-CHO-EFNB2-BA6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-1_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-2_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-3_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231207-CHO-EFNB2-BA6-pool_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231222-CHO-EFNB2-BA6-nac_diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231112-CHO-EFNB2-BA6-nac_diff_VSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231116-CHO-BA6_PREV_POOL_func_effects.csv Processing file: results/func_effects/by_selection/LibA-230725-CHO-EFNB3-C6-nac-diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibA-230916-CHO-EFNB2-BA6-nac_diffVSV_func_effects.csv Processing file: results/func_effects/by_selection/LibA-231024-CHO-EFNB3-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230720-CHO-C6-nac-VSV_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230815-CHO-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-230818-CHO-C6-nac_func_effects.csv Processing file: results/func_effects/by_selection/LibB-231116-CHO-C6_PREV_POOL_func_effects.csv
Now make histogram of variants¶
In [11]:
codon_variants = pd.read_csv(codon_variants_file)
display(codon_variants.head(3))
unique_barcodes_per_library = codon_variants.groupby('library')['barcode'].nunique()
uniq_barcodes_per_lib = pd.DataFrame(unique_barcodes_per_library)
display(uniq_barcodes_per_lib)
| target | library | barcode | variant_call_support | codon_substitutions | aa_substitutions | n_codon_substitutions | n_aa_substitutions | |
|---|---|---|---|---|---|---|---|---|
| 0 | gene | LibA | AAAAAAAAAAAAAGAA | 5 | ACC461ACT ATC475AGC | I475S | 2 | 1 |
| 1 | gene | LibA | AAAAAAAAAAACCCAT | 36 | GCG16GAG CAG23GAG | A16E Q23E | 2 | 2 |
| 2 | gene | LibA | AAAAAAAAAAAGTTTC | 6 | TAC319CCC | Y319P | 1 | 1 |
| barcode | |
|---|---|
| library | |
| LibA | 78450 |
| LibB | 60623 |
Find which sites are present, and which are missing¶
In [12]:
# Initialize an empty dictionary to keep track of observed mutations
aa_counts = {}
wildtypes = {} # Dictionary to keep track of wildtype amino acids for each site
# Function to process each cell, update counts, and record wildtype amino acids
def process_cell(cell):
if pd.notna(cell): # Check if cell is not NaN
substitutions = cell.split()
for substitution in substitutions:
if substitution[-1] not in ('*', '-') and substitution[0] not in ('*'): # Skip if substitution ends with '*' or '-'
site = substitution[1:-1]
mutation = substitution[-1]
wildtype = substitution[0]
site_mutation = site + mutation
if site not in wildtypes:
wildtypes[site] = wildtype
if site_mutation in aa_counts:
aa_counts[site_mutation] += 1
else:
aa_counts[site_mutation] = 1
empty_mutants = []
empty_percent = []
for lib in ['LibA','LibB']:
# Apply the function to each cell in the 'aa_substitutions' column
tmp_df = codon_variants[codon_variants['library'] == lib]
tmp_df['aa_substitutions'].apply(process_cell)
# Generate all possible combinations excluding the wildtype for each site
expected_sites = range(1, 533)
possible_mutations = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
# Adjust expected combinations to exclude the wildtype for each site
expected_combinations = set()
for site in expected_sites:
site_str = str(site)
if site_str in wildtypes:
wildtype = wildtypes[site_str]
for mutation in possible_mutations:
if mutation != wildtype: # Exclude the wildtype amino acid
expected_combinations.add(site_str + mutation)
# Extract the actual combinations from the counts
actual_combinations = set(aa_counts.keys())
# Find missing combinations
missing_combinations = expected_combinations - actual_combinations
# Display results
print(f"Number of unique site-mutation combinations observed: {len(aa_counts)}")
print(f"Number of missing combinations (excluding wildtypes): {len(missing_combinations)}")
print(f"Total possible combinations excluding wildtypes: {len(expected_combinations)}")
empty_percent.append(len(actual_combinations) / len(expected_combinations))
#empty_percent = empty_percent.reset_index()
uniq_barcodes_per_lib['percent'] = empty_percent
uniq_barcodes_per_lib['percent'] = uniq_barcodes_per_lib['percent'] * 100
uniq_barcodes_per_lib = uniq_barcodes_per_lib.round(2)
uniq_barcodes_per_lib = uniq_barcodes_per_lib.reset_index()
uniq_barcodes_per_lib.to_csv(uniq_barcodes_per_lib_df,index=False)
Number of unique site-mutation combinations observed: 10055 Number of missing combinations (excluding wildtypes): 54 Total possible combinations excluding wildtypes: 10108 Number of unique site-mutation combinations observed: 10080 Number of missing combinations (excluding wildtypes): 29 Total possible combinations excluding wildtypes: 10108
In [13]:
def calculate_fraction(library):
total_A = codon_variants[codon_variants['library'] == library].shape[0]
for number in range(5):
fraction = codon_variants[(codon_variants['library'] == library) & (codon_variants['n_aa_substitutions'] == number)].shape[0]
print(f'For {library}, the fraction of sites with {number} mutations relative to WT are: {fraction/total_A:.2f}')
calculate_fraction('LibA')
calculate_fraction('LibB')
For LibA, the fraction of sites with 0 mutations relative to WT are: 0.11 For LibA, the fraction of sites with 1 mutations relative to WT are: 0.64 For LibA, the fraction of sites with 2 mutations relative to WT are: 0.22 For LibA, the fraction of sites with 3 mutations relative to WT are: 0.03 For LibA, the fraction of sites with 4 mutations relative to WT are: 0.00 For LibB, the fraction of sites with 0 mutations relative to WT are: 0.11 For LibB, the fraction of sites with 1 mutations relative to WT are: 0.65 For LibB, the fraction of sites with 2 mutations relative to WT are: 0.21 For LibB, the fraction of sites with 3 mutations relative to WT are: 0.03 For LibB, the fraction of sites with 4 mutations relative to WT are: 0.00
In [14]:
def plot_histogram(df):
df = df.drop(columns=['barcode','target','variant_call_support','codon_substitutions','aa_substitutions','n_codon_substitutions'])
chart = alt.Chart(df).mark_bar(color='black').encode(
alt.X("n_aa_substitutions:N",title='# of AA Substitutions'),
alt.Y('count()', title='Count',axis=alt.Axis(grid=True)), # count() is a built-in aggregation to count rows in each bin
column=alt.Column('library',header=alt.Header(title=None, labelFontSize=18))
)
return chart
histogram = plot_histogram(codon_variants)
histogram.display()
if histogram_plot is not None:
histogram.save(histogram_plot)
Find distribution of functional scores¶
In [15]:
def pull_in_func_scores(df):
empty_list = []
for i in df:
j = i + '_func_scores.csv'
tmp_df = pd.read_csv(f'results/func_scores/{j}')
tmp_df['selection'] = i
empty_list.append(tmp_df)
tmp_df = pd.concat(empty_list)
return tmp_df
e2_func_scores_df = pull_in_func_scores(cho_efnb2_low_selections)
e2_func_scores_df['cell_type'] = 'CHO-EFNB2'
e3_func_scores_df = pull_in_func_scores(cho_efnb3_low_selections)
e3_func_scores_df['cell_type'] = 'CHO-EFNB3'
#Make combined dataframe of cell entry data
merged_func_scores = pd.concat([e2_func_scores_df,e3_func_scores_df])
def classify_mutation(row):
if isinstance(row['aa_substitutions'], str) and '*' in row['aa_substitutions']:
return 'stop'
elif row['n_aa_substitutions'] == 0:
if row['n_codon_substitutions'] >= 1:
return 'synonymous'
else:
return 'wildtype'
elif row['n_aa_substitutions'] == 1:
return '1 nonsynonymous'
elif row['n_aa_substitutions'] >= 2:
return '>2 nonsynonymous'
# Apply the function to each row in the dataframe to create the new column
merged_func_scores['mutation_class'] = merged_func_scores.apply(classify_mutation, axis=1)
result_df = merged_func_scores.groupby(['barcode','cell_type']).agg(
func_score=('func_score', 'median'),
mutation_class=('mutation_class','first')
).reset_index()
tmp = result_df.groupby(['mutation_class','cell_type'])['func_score'].median().reset_index()
tmp = tmp.rename(columns={'func_score':'median_func_score'})
result_df = result_df.merge(tmp,on=['mutation_class','cell_type'],how='left')
display(result_df)
| barcode | cell_type | func_score | mutation_class | median_func_score | |
|---|---|---|---|---|---|
| 0 | AAAAAAAAAAGACCCG | CHO-EFNB2 | -2.20700 | >2 nonsynonymous | -1.8130 |
| 1 | AAAAAAAAAAGACCCG | CHO-EFNB3 | 0.03215 | >2 nonsynonymous | -3.0560 |
| 2 | AAAAAAAAACCTATAG | CHO-EFNB2 | -0.17420 | 1 nonsynonymous | -0.6460 |
| 3 | AAAAAAAAACCTATAG | CHO-EFNB3 | -0.85940 | 1 nonsynonymous | -0.9497 |
| 4 | AAAAAAAAATCCTACG | CHO-EFNB2 | -0.86490 | 1 nonsynonymous | -0.6460 |
| ... | ... | ... | ... | ... | ... |
| 128340 | TTTTTTCGATGAACGA | CHO-EFNB3 | -4.15400 | >2 nonsynonymous | -3.0560 |
| 128341 | TTTTTTGCCAAGTGAA | CHO-EFNB2 | -0.50200 | >2 nonsynonymous | -1.8130 |
| 128342 | TTTTTTTAAGACTACA | CHO-EFNB3 | -2.23100 | 1 nonsynonymous | -0.9497 |
| 128343 | TTTTTTTACTCGAATG | CHO-EFNB2 | -0.46220 | 1 nonsynonymous | -0.6460 |
| 128344 | TTTTTTTACTCGAATG | CHO-EFNB3 | 1.59500 | 1 nonsynonymous | -0.9497 |
128345 rows × 5 columns
In [16]:
def plot_func_score_distribution(df):
custom_sort = ['wildtype', 'synonymous', '1 nonsynonymous', '>2 nonsynonymous', 'stop']
empty_charts = []
for cell_idx,target_cell in enumerate(['CHO-EFNB2','CHO-EFNB3']):
charts = []
first_df = df[df['cell_type'] == target_cell]
for idx, subset in enumerate(custom_sort):
tmp_df = first_df[first_df['mutation_class'] == subset]
is_last_plot = idx == len(custom_sort) - 1
x_axis = alt.Axis(labelAngle=-90, titleFontSize=10,tickCount=3, values=[-10, -5, 0],
title="Functional Score" if is_last_plot else None,
labels=True if is_last_plot else False) # Only show labels for the last plot
first_plot_column = cell_idx == 0
y_axis = alt.Axis(labelAngle=0,titleAngle=0,title=subset if first_plot_column else None,domain=False,ticks=False,labels=False,titleX=-10,titleAlign='right')
chart = alt.Chart(tmp_df,title=(target_cell if idx == 0 else "")).mark_area(color='black').encode(
x=alt.X('func_score', bin=alt.Bin(step=0.4), axis=x_axis),
y=alt.Y('count()', title=subset, axis=y_axis),#alt.Axis(domain=False, ticks=False, labels=False)),
color=alt.Color('median_func_score',title='Median Functional Score',scale=alt.Scale(scheme='greenblue')),
#row=alt.Row('mutation_class', title=None, sort=custom_sort, header=alt.Header(title=None)),
#column=alt.Column('cell_type'),
).properties(width=100, height=50)
charts.append(chart)
combined_muts_chart = alt.vconcat(*charts,spacing=0).resolve_scale(y='independent',x='shared',color='shared')
empty_charts.append(combined_muts_chart)
# Combine charts using vertical concatenation, adjusting scales and configuration as needed
combined_chart = alt.hconcat(*empty_charts, spacing=0).resolve_scale(
y='independent', x='shared', color='shared'
).configure_view(
stroke=None
).configure_axis(
grid=False
).configure_title(
anchor='middle', # Anchors the title to the start of the chart
offset=5, # Adjusts the distance between the title and the chart
fontSize=16, # Adjusts the font size of the title
#dx=5, # Shifts the title horizontally (use negative value to shift left)
#dy=-5 # Shifts the title vertically (use negative value to shift up)
)
return combined_chart
tmp_img = plot_func_score_distribution(result_df)
tmp_img.display()
if histogram_plot is not None:
tmp_img.save(func_scores_plot)